4 accepted papers
Real-world reinforcement learning demands adaptation to unseen environmental conditions without costly retraining. Contextual Markov Decision Processes (cMDP) model this challenge, but existing methods often require explicit context variables (e.g., friction, gravity), limiting their use when contex…
We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neur…
We present an illustrative example of an interactive auditory perception approach performed by a humanoid robot called NICO, the Neuro Inspired COmpanion [1]. The video demonstrates a material classification task in the style of a classic TV game show. NICO and another candidate are supposed to dete…
We are developing a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction…